Machine learning and artificial intelligence are all the rage today in venture capital circles. We've seen spectacular exits in the past few years, from Google absorbing Deepmind in 2014 for $500 million, to Twitter buying TellApart in 2015 for $533 million, and Intel swallowing Nervana in 2016 for $400 million. But these were all IT plays. Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals. Ultimately, the ability to design and optimize microbes, or program them, is becoming faster and cheaper than ever before.

This year, we kicked things off by telling you how deadly and difficult space is to explore. It can kill you with radiation, giant flying space rocks, and regular old time. And those are just a few of its weapons. But while space is a pretty dangerous place, it's also incredibly inspirational. If you take science fiction as your model--which we often do--people are at their best when faced with a seemingly insurmountable challenge.

Each of these trucks is the size of a small two-story house. None has a driver or anyone else on board. Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. At this one, known as West Angelas, the vehicles work alongside robotic rock drilling rigs. The company is also upgrading the locomotives that haul ore hundreds of miles to port--the upgrades will allow the trains to drive themselves, and be loaded and unloaded automatically.

This ghost-like octopus was just discovered last year off the coast of Hawaii, but already scientists think it might be in danger. These octopods lay their eggs on one particular type of sponge that grows on top of manganese deposits. Manganese is currently a heavily-mined mineral because of its usefulness in producing mobile phones and other tech gadgets. For now, because of a sufficient amount of manganese on Earth's surface, these animals are safe. But scientists don't know for how long.

Machine learning and artificial intelligence are all the rage today in venture capital circles. We've seen spectacular exits in the past few years, from Google absorbing Deepmind in 2014 for $500 million, to Twitter buying TellApart in 2015 for $533 million, and Intel swallowing Nervana in 2016 for $400 million. But these were all IT plays. Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals. Ultimately, the ability to design and optimize microbes, or program them, is becoming faster and cheaper than ever before.

It is likely self-evident to many that the security industry's most overused buzzword of the year is "machine learning." Yet, despite the ubiquity of the term and its presence in company marketing literature, most people – including those working for many of the vendors using the term – don't actually know what it means. Scanning through industry sites and product descriptions, machine learning is often positioned as either a "new" tool or a "new" method – something that can provide additional capabilities or features. For many classes of threat detection, machine learning is positioned as "signatureless" detection by those that don't yet know the basic principles of the math or science behind it. The best way to understand what machine learning is and what it truly brings to the security industry is to compare it to a technology advance that kick-started two centuries ago – the steel age.

In a previous article, we had an overview of the applications of Deep Learning and touched upon some basic points to consider while creating a Deep Learning model. We also had an overview of what it is and methods to get started with deep learning. In this article, we jump straight into creating an anomaly detection model using Deep Learning and anomaly package from H2O. Readers who don't know what it is can view it as anything that occurs unexpected and is a rare event. It is a deviation from the standard pattern and does not confirm to the usual behavior of the data. Let's say we work in a steel manufacturing industry, and we see the quality of the steel suddenly drops down below the permissible limits. This is an anomaly; if not detected and resolved soon will cost the organization millions.

Mars is currently inhabited by an estimated 1 million microbes. They coat the surfaces and crowd the innards of our robotic landers and rovers, which international policy requires to be cleaned, but not fully sterilized. The bugs are dormant, but viable. If Mars warmed up and water began to flow again, these microorganisms would revive and reproduce. And it is within our power to make that happen.

Click to comment on this story. Click to comment on this story. Give your dinner party some gravitas by placing this stunning centerpiece among the sides and sauces. The beautiful jewel-toned object has a concave scoop in the center, providing a reflective surface that encourages deep thought--or offers an awesome place to toss your keys. It can control your entire home--commanding 380,000 devices in all, from TVs and game consoles to wireless speakers and streaming devices. Just try not to lose this one, OK?

Synthetic biology ("synbio") initially attracted high-profile venture capitalists and corporate partnerships for its products – bio-based chemicals and alternative fuels, for example. Today, synbio companies tools are attracting attention as well – from lab automation like DNA synthesis-as-a-service, to software (i.e. By integrating machine-learning, robotics, and high-throughput engineering, companies like Ginkgo Bioworks and Zymergen are helping customers dramatically reduce the timeline for developing new molecules in the flavor and fragrance, food ingredient, medical, agricultural, and specialty chemicals markets – a key reason that those two startups each raised more than $100 million in 2016. This podcast highlights Lux's thoughts on the impact of these tools in and beyond in synbio, and the rapidly developing ecosystem of startups within the space.